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A First-Order Interior-Point Method for Linearly Constrained Smooth Optimization

Paul Tseng(tseng***at***math.washington.edu)
Immanuel M. Bomze(immanuel.bomze***at***univie.ac.at)
Werner Schachinger(werner.schachinger***at***univie.ac.at)

Abstract: We propose a first-order interior-point method for linearly constrained smooth optimization that unifies and extends first-order affine-scaling method and replicator dynamics method for standard quadratic programming. Global convergence and, in the case of quadratic programs, (sub)linear convergence rate and iterate convergence results are derived. Numerical experience on simplex constrained problems with 1000 variables is reported.

Keywords:

Category 1: Nonlinear Optimization (Other )

Category 2: Nonlinear Optimization (Quadratic Programming )

Citation: Technical Report TR-ISDS {\bf 2007-17}, University of Vienna (2007).

Download: [PDF]

Entry Submitted: 11/28/2007
Entry Accepted: 11/28/2007
Entry Last Modified: 11/28/2007

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